info:eu-repo/semantics/article
Fisher Vectors for PolSAR Image Classification
Fecha
2017-11Registro en:
Redolfi, Javier Andrés; Sanchez, Jorge Adrian; Flesia, Ana Georgina; Fisher Vectors for PolSAR Image Classification; Institute of Electrical and Electronics Engineers; Ieee Geoscience and Remote Sensing Letters; 14; 11; 11-2017; 2057-2061
1545-598X
CONICET Digital
CONICET
Autor
Redolfi, Javier Andrés
Sanchez, Jorge Adrian
Flesia, Ana Georgina
Resumen
In this letter, we study the application of the Fisher vector (FV) to the problem of pixelwise supervised classification of polarimetric synthetic aperture radar images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real parts of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive an FV from a mixture of real Wishart densities and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging data sets show the effectiveness of the approach.